Work

Parallel ACO-VNS for the solution of binary combinatorial optimization problems in computational systems biology

Year: 2026

Type: article

Abstract: In this work, a parallel hybrid framework that combines Ant Colony Optimization (ACO) with Variable Neighborhood Search (VNS) to enhance both exploration and intensification is proposed. The method integrates a multicolony architecture with a self-adaptive cooperation mechanism that dynamically regulates information exchange among colonies during runtime. This design aims to overcome the limitations of standalone ACO while maintaining scalability on high-performance computing platforms. The proposed approach is evaluated on a set of challenging large-scale binary optimization benchmarks derived from cell signaling network modeling. Experimental results show that the ACO–VNS hybrid consistently outperforms standalone ACO. The results demonstrate the effectiveness and general applicability of the proposed framework for complex binary combinatorial optimization problems.

Source: Applied Soft Computing

DOI: 10.1016/j.asoc.2026.115422

Authors: Roberto Prado-Rodríguez, Patricia González, Julio R. Banga, Ramón Doallo

Institutions: Universidade da Coruña, Consejo Superior de Investigaciones Científicas, Misión Biológica de Galicia

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,730

Funders: Ministerio de Ciencia, Innovación y Universidades, Consellería de Cultura, Educación e Ordenación Universitaria, Xunta de Galicia